Markerless Motion Analysis Using New Digital Technology.

Physical therapy research Pub Date : 2025-01-01 Epub Date: 2025-07-03 DOI:10.1298/ptr.R0037
Megumi Ota
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Abstract

Motion analysis is essential for physical therapists and athletic trainers to understand the motor function of their patients or athletes. Although marker-based motion analysis systems have been widely utilized in research, they are expensive and demand significant time and effort for measurement and analysis, which can complicate their application in clinical practice. In recent years, markerless motion analysis technologies have emerged as affordable and portable alternatives. These technologies include inertial measurement unit (IMU) sensors, depth cameras, manual digitization, and posture-tracking algorithms. IMU sensors detect motion using accelerometers and gyro sensors and can be worn on body parts. Depth cameras use infrared or laser technology to capture three-dimensional (3D) motion without requiring markers. Manual digitization enables semiautomatic identification of joint positions from images, allowing joint angle measurement without using specific cameras or markers. Posture-tracking algorithms use artificial intelligence to approximate joint positions from standard camera images, enabling automated motion analysis. Despite the enhanced accessibility of these technologies, limitations remain, particularly in analyzing detailed joint movements or individuals with structural abnormalities, and their accuracy depends on the environment and motion task. However, with further development, these technologies are expected to become increasingly reliable and provide physical therapists and athletic trainers with valuable, cost-effective, and easy-to-use tools for assessing movement in clinical and sports settings.

Abstract Image

Abstract Image

基于新数字技术的无标记运动分析。
运动分析是必不可少的物理治疗师和运动教练了解他们的病人或运动员的运动功能。尽管基于标记物的运动分析系统在研究中得到了广泛的应用,但它们价格昂贵,需要花费大量的时间和精力进行测量和分析,这使得它们在临床实践中的应用变得复杂。近年来,无标记运动分析技术已成为经济实惠和便携式的替代方案。这些技术包括惯性测量单元(IMU)传感器、深度相机、手动数字化和姿态跟踪算法。IMU传感器使用加速度计和陀螺仪传感器检测运动,可以佩戴在身体部位。深度相机使用红外或激光技术捕捉三维(3D)运动而不需要标记。手动数字化可以从图像中半自动识别关节位置,无需使用特定的相机或标记即可测量关节角度。姿势跟踪算法使用人工智能从标准相机图像中近似关节位置,从而实现自动运动分析。尽管这些技术提高了可及性,但仍然存在局限性,特别是在分析关节运动或结构异常个体的详细情况时,其准确性取决于环境和运动任务。然而,随着进一步的发展,这些技术有望变得越来越可靠,并为物理治疗师和运动教练提供有价值的、具有成本效益的、易于使用的工具,以评估临床和运动环境中的运动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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